R help - Mar 2010 - Multilevel modeling with count variables

I am using a multilevel modeling approach to model change in a person's
symptom score over time (i.e., longitudinal individual growth models).  I
have been using the lme function in the multilevel package for the analyses,
but my problem is that my outcome (symptoms) and one of my predictors
(events) are count data, and are non-normal.  Do you have any suggestions
for how to deal with them?  Are there poisson-regression or similar
techniques that can be applied to multilevel modeling (lme, nlme, etc.)? 
Some of my predictors are normal, however, so the technique should be able
to accommodate normally distributed data, as well.  I have tried square root
transformations, and it appears that the data become more normal, but at the
expense of some of the consistency in my findings modeling the raw data. 
Your input would be very helpful and greatly appreciated.  Thanks so much!
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By the way, my concern with lmer and glmer is that they don't produce
p-values, and the techniques used to approximate the p-values with those
functions (pvals.fnc, HPDinterval, mcmcsamp, etc.) only apply to Gaussian
distributions.  Given that I would likely be working with quasi-poisson
distributions, is there a good alternative mixed effects modeling approach
that would output significance values?  Thanks!
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